Tumor characteristics impact prognosis in deficient mismatch repair/microsatellite instability-high localized colorectal cancer—a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Deficient mismatch repair (dMMR) and microsatellite instability-high (MSI-H) tumors constitute ∼15% of localized colorectal cancers (CRCs). Prognostic biomarkers such as tumor-infiltrating lymphocytes (TILs) and BRAF and KRAS mutations may guide personalized treatment for these patients, and this systematic review and meta-analysis aimed to evaluate their impact on survival outcomes. METHODS: Literature searches were conducted across PubMed, Embase, Cochrane Library, and Web of Science, including studies published between 2004 and 2023. The primary outcomes were overall survival (OS), disease-free survival (DFS), and cancer-specific survival. The risk of bias was assessed using the Newcastle-Ottawa Scale, and the certainty of evidence using the GRADE approach. RESULTS: The literature search yielded 5636 articles. Fifty-four studies were included in the systematic review and 31 studies in the meta-analysis, totaling 4551 patients. High TIL density was significantly associated with improved OS (hazard ratio [HR] = 0.39, 95% CI = 0.17 to 0.89) and DFS (HR = 0.45, 95% CI = 0.29 to 0.71). BRAF and KRAS mutations were seen in 52% and 34% of patients, respectively, and were associated with poorer OS (HR = 1.43, 95% CI = 1.13 to 1.80 and HR = 1.30, 95% CI = 1.09 to 1.54, respectively). Quality of evidence was moderate to high across all exposures and outcomes. CONCLUSION: High infiltration of TILs correlated with improved OS and DFS, whereas BRAF and KRAS mutations were associated with worse OS in patients with localized dMMR/MSI-H CRC. These findings highlight the potential utility of biomarkers for improving prognostic assessment and personalizing management in dMMR CRC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".